Semantic technologies involve the use of semantic modeling and tools to structure and provide information based on an understanding of human language and behavior. Traditional software systems are configured to obtain machine-readable commands and information as input in order to function. These systems typically deal extensively with human readable information, yet lack the capability to interpret or otherwise refine that information. This has lead to a structural dichotomy of data into machine-readable and human readable forms. This underlying framework of system design leaves users, designers, and developers with the task of translation, interpretation, and modification of human-readable data into machine-readable form, and vice-versa. Semantic technologies attempt to bridge the divide by allowing systems to interpret, modify, and/or generate human readable information.
The preparation and filing of financial returns involves complex financial calculations and relationships between various financial amounts among multiple forms. Traditional software systems are well adapted for storing, calculating, and accessing this type of relational data. Despite this, even with the aid of a tax preparation application, the process of preparing and filing a financial return can be extremely difficult for the user. This difficulty is due, at least partially, to the fact that, aside from entering financial amounts, the user is required to read and understand a series of interview-based questions presented by the application. This communication between the user and the application is substantially based on the machine-readable language of the application. In other words, the options presented to the user are static, and are based solely on the underlying requirements of the tax document, while the behavior and attributes of the user are largely ignored.